{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ReservoirFractionalFlowModel.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMeqC0N580H1ADbFUtAsp8M",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/ReservoirFractionalFlowModel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGe8-khbtp2a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#FRACTIONAL FLOW SIMULATION\n",
        "#A clear depiction of how the fractional flow curve varies as we change the properties. "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ggUHZ_dqt2Pb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDLjoLgsuLxA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sw = np.linspace(0.01,1,num=50)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8bC-z0oCu1y8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "so = 1-sw"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "084CeMgpvCF8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "krw = sw**3"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zMMNFuOmvDPD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "kro = (1-sw)**3"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aU9Jr1t0v-zP",
        "colab_type": "code",
        "outputId": "3ac0a295-fc49-452e-a553-fd806f23ca04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "plt.plot(sw,krw)\n",
        "plt.plot(sw,kro)\n",
        "plt.xlabel('Water Saturation, Sw')\n",
        "plt.ylabel('Relative permeability,Krel')\n",
        "plt.legend(['Krw','Kro'])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fe17fdb2f28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HJEwVJa_waX_",
        "colab_type": "code",
        "outputId": "4ecaaf54-a2a3-4052-ae07-86b90db7db47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        }
      },
      "source": [
        "#Now fractional flow part for a horizontal flow\n",
        "for i in range(3):\n",
        "  uo = input(\"\\nenter oil viscocity!: \")\n",
        "  uo = int(uo)\n",
        "  uw = input(\"\\nenter water viscocity!: \")\n",
        "  uw = int(uw)\n",
        "  fw = 1/(1+(uw*kro)/(uo*krw))\n",
        "  plt.plot(sw,fw)\n",
        "  plt.xlabel('Water saturaction, Sw')\n",
        "  plt.ylabel('fractional flow of water, fw(bbl/bbl)')\n",
        "  plt.legend(['1 CP water','100 CP (polymer) water','1000 CP polymer flood'])\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "enter oil viscocity!: 10000\n",
            "\n",
            "enter water viscocity!: 1\n",
            "\n",
            "enter oil viscocity!: 10000\n",
            "\n",
            "enter water viscocity!: 100\n",
            "\n",
            "enter oil viscocity!: 10000\n",
            "\n",
            "enter water viscocity!: 1000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cTmH31pQyCy4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Mobility ratio M = M(displacing)/M(displaced) = krw*uo/(kro*uw)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ci9JUc0eybVO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# fw = 1/(1+(uw*kro)/(uo*krw))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WMSF1K6Ny56n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# plt.plot(sw,fw)\n",
        "# plt.xlabel('Water saturaction, Sw')\n",
        "# plt.ylabel('fractional flow of water, fw(bbl/bbl)')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x08hsJ4lzWB7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}